"A good platform for sketching and drawing"
It is an excellent wire framing tool for work area.It is very easy and comprehensible software. The features are extremely handy which you can understand quickly and helps you become pro soon. The setup is very very easy and simple to modify which helps you in easy editing. Now you can sketch with amazing tools and designs by this software.
It is a good software to use so found no dislikes in it yet. It is easy to use with great features.
A good platform that assists you in making your art practice better and better. Helps you create and innovate amazing ideas for better creativity. If you are a client that needs a sketch and drawing then it is the best choice for you. It is a must try software specially who are keen in arts. A very recommendable software.
A good tool for the representation of sketch. Helps you in planning your and practicing your basics and get them better. Good in prototyping and emphasizing structures. Has excellent tools and features that helps you in innovation and and better graphics. Helps you flourish your art business and makes it prosperous.
"A great web unit for sketching"
It is a great and easy to use software. The segments are to the point, attractive and very easily understanding. It is a great wire framing pack for the work area that has got all the imperative essentials for sorting out any interface you are taking a shot at. All is perfectly setup finely and it is very simple to modify.
It should add more tools or choices in the list of capability. Otherwise it is an extra ordinary software for use.
It is a great software that helps you in making your sketch more creative and attractive. If you are a sketch client then it is the best choice for you. You must give it a shot for your work. It is easy to use and time saving.
It is a great software for the representation of sketch. It allows your to go with extreme basic, plain pictures that can be edited as required. Helps in prototyping and emphasizing structures.
Greater control in web layout designs, this Wireframes provides simple design options to present and adopt a unique design without losing much time, you can make the changes in a quick and easy way to elaborate it later. Tracy is free from GIT and easy to use, just copy and paste the item(s) or select and modify them.
It's the biggest downside is also kind of a positive. It has so many options that navigating them to find what you need can be a bit of a pain. It was a little difficult for me to navigate the website. I was overwhelmed and needed an easier searching feature. I think it's user error on my part though.
Many times it is very difficult to present the design you want in the orders of web designs and we lose a lot of time adjusting the design, but with this wireframe, we channel and present the designs effectively without so many complications.
""An incredible arrangement of highlights to make wireframing less demanding""
The segments are point by point, outwardly engaging, easy to utilize, and copious. It's anything but difficult to get moving, and simple to ceaselessly repeat once you get client/customer input. Anticipating perceiving how Teracy works in the future.Finally! A wireframing pack that is intended for work area. Has all the imperative essentials you have to put together for practically any sort of interface you are taking a shot at. Everything is set up perfectly fine, and simple to modify.
It is great to add extra choices to the list of capabilities. I found a couple of cases where there was certifiably not a module I required. It is extraordinary to likewise indicate alternatives for association focuses (for example drop down, haul out and so on) - this sort of thing truly encourages customers to get a more profound comprehension of their sites ease of use.
Teracy is a web unit add-on so to represent Sketch. You have to download it from GIT and spot it in a spot you can discover it with each utilization and not alter the first. It is allowed to utilize yet has extremely basic, plain pictures in it that can be moded as required and not every single social medium symbols/pictures are included.This item is one of the better ones out there. Certainly worth thought when searching for wireframing instruments that will make your life simpler. In the event that you're a Sketch client, at that point this is an easy decision - certainly give it a shot in your next task.
"Excelent kit for Wiframe Developemnt"
for web development its most efficient but for mobile application its ui kit must be updated.
"Greater effectiveness in web layout in less time possible."
Greater control in web layout designs, this Wiframes provides simple design options to present and adopt a unique design without losing much time, you can make the changes in a quick and easy way to elaborate it later.
Some tool hinders the free design of each element since it already specifies the areas in which it is going to be applied and it does not leave the methods free, therefore you have to resort from time to time codes.
Every web developer must have some tool to help them in the web layout, this is one of the most simple but very effective.
Many times it is very difficult to present the design you want in the orders of web designs and we lose a lot of time adjusting the design, but with this wireframe we channel and present the designs effectively without so many complications.
"Wireframe UI Kit Sketch Resource."
Almost all the elements of the user interface become a symbol so that they can be reused in all projects.
It has many options that are not very useful, we should simplify the search and reduce the necessary time.
It is always a good idea to start with a wireframe when starting a new web design or a mobile application project. The wires can be a great time saver, as well as help the designers and the developers to reach an agreement on the placement of the main elements of the user interface. The key is to make the design become a place.
In our design process in Teracy, wireframings and models take an important position. We had developed a wireframe kit to accelerate the design process and use it in our daily design workflow.
Teracy is free from GIT and easy to use, just copy and paste the item(s) or select and modify them.
Teracy's Web Kit designs are all extremely simple and plain.
Teracy is a web kit add-on so to speak for Sketch. You need to download it from GIT and place it in a place you can find it with each use and not modify the original. It is free to use but has very simple, plain images in it that can be moded as needed and not all social media icons/images are included.
Teracy added more icons, symbols and extras to Sketch so that I could improve my Web designs. There are several socail media buttons included in it along with othere web design must haves.
It is a good kit that works good with sketch. the features are attractive, simple, uncomplicated and very easy to use even for the beginners. It has got all the basics which you need for an interface that you are working on.
Some of the features don't go along with the others due to which it gets a little unappealing. Would prefer to have everything sized up to the baseline.
The people who have not used Teracy should give it a try because it is a great wire framing kit, with very appealing and interesting features.. It is very helpful even for the beginners thus saves time.
For business, it is an excellent initiative with creative features and innovations. It gives hope for a beautiful future with some adjustments.
"Built for speed, and lives up to it's promise"
Teracy is a wireframing kit that works quite well with Sketch. It's built for speedy wireframes, and it lives up to that. It's a no frills kit with lots of options, and none of it is particularly visually dazzling - but wireframes don't need to be.
It's biggest downside is also kind of a positive. It has so many options that navigating them to find what you need can be a bit of a pain.
If you're looking for a no frills UI Kit that can help you quickly wireframe and iterate on your designs, this is great.
It's enabling me as a UX designer to do exactly what I need to be doing. Prototyping and iterating on designs, quickly.
Best for beginning a new webpage for a website or a mobile app. Easy and rapid to create layouts and wireframes. Hence, this helps speed up the designing and maintenance of that design uniformly throughout the project or various projects if other clients like your layout/wireframe. Best for reusability and rapid development of webpages. And the best part is the icons that are visually pleasing!
Mobile device elements needs to be updated. Good for webpages but needs improvement for mobile app UI development.
The program does lack some things. The customer care is lacking. The customer service could be easier to mange and get to.
Our team has been loving this and we have be able to solve a lot of work places issues.
"A great set of features to make wireframing easier"
The Teracy Wireframe kit makes wireframes with Sketch a breeze. Lots of options and the ability to export for other services makes the whole process streamlined.
The components are detailed, visually appealing, simple to use, and plentiful. It's easy to get going, and easy to continually iterate once you receive user/client feedback. Looking forward to seeing how Teracy builds in the future.
It would be good to add additional options to the feature set. I found a few instances where there wasn't a module I required. It would be great to also show options for interaction points (e.g. drop down, pull out etc) - this type of thing really helps clients to get a deeper understanding of their websites usability.
This product is one of the better ones out there. Definitely worth consideration when looking for wireframing tools that will make your life easier. If you're a Sketch user then this is a no-brainer - definitely give it a shot in your next project.
I was looking for courses on coding. I have to teach programming at my work, but I am not a formally trained programmer. I like that there are courses available for every level. The sign up process was easy.
It was a little difficult for me to navigate the website. I was overwhelmed and needed an easier searching feature. I think it's user error on my part though.
Teracy isn't really solving anything for me yet. I'm a beginning user. I think there are benefits though. It's great for the connection to a community that I couldn't reach otherwise.
Its free and easy to use. It is also very plain and simple and therefore easy to understand. It saves a lot of time. It has many options to choose from and once you go through the functionality, its almost too easy to get work done.
It has many tools to improve web layout and change design. This also supports prototyping, so you can just choose to elaborate later.
It is great to add extra choices to the list of capabilities.
Teracy included more symbols, images, and additional items to sketch with the goal that I could enhance my web structures.
It is very easy to get going with this software.
Not easy to navigate website for new users.
Teracy is free from GIT and it is very easy to use. The simple way to do the work is that simply copy and paste the items or select the modify the items. The segments are point to point outwardly engaging, easy to utilise and copious.
Teracy Web kit designs are all extremely simple and plain.
Teracy includes more symbols, images and additional items to sketch with the goals that could I enhance my Web structures.
"Great desktop kit with all the basics, but odd proportions"
Finally! A wireframing kit that is meant for desktop. Has all the important basics you need to throw together for almost any type of interface you are working on. Everything is set up right as symbols, and easy to customize.
Some widgets don't seem to be proportionate to others when put together as a layout, and it self has an opinion how big different elements should be relative to others. Would have preferred everything to be sized to a similar baseline/multiple.
If you are a little OCD (which designer isn't?), the "clean" wireframe style can be distracting when you just want to throw stuff together quickly and ignore how the type is set.
It's a great start, but needs some customization work to suit your preference/requirements.
I would like to see Teracy Expand to more UI and wireframing assets. Working within the digital learning world it can be a struggle to find all of the pieces and functionalities that we use in our products.
I got Teracy is free from GITHUB its easy to use, Lots of options and the ability to export for other services makes the whole process streamlined. Teracy speed up my wire framing with some generic pieces that we would commonly use.
It already packed with the most used UI-Components to quickly start wireframing.
It would be nice to have an Icon font included to quickly select Icons for use on buttons … Also responsive elements and behavior could be improved.
Makes all my wireframing significantly easier.
The GIT is limited in the capabilities I need it for.
Enhanced my Sketch experience, making design a breeze.
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Hi there! Are you looking to implement a solution like Teracy?


text:
Journal of Aviation/Aerospace Education & Research
Home > Journals and Magazines > Journals > JAAER > Vol. 15 > No. 2 (2006)
JAAER Winter 2006
Front Matter/Back Matter
Impact of Glass Cockpit Experience on Manual Flight Skills
John P. Young, Richard O. Fanjoy, and Michael W. Suckow
Stuck in the Middle Revisited: The Case of the Airline Industry
Isabelle Dostaler and Triant Flouris
The Application of Scenario Based Recurrent Training to Teach Single Pilot Resource Management (SRM) Under the FAA Industry Training Standards (FITS) Program
Francis H. Ayers
Editor's Forum
Bill Kohlruss
Using Informal Student Feedback to Enhance Learning
Antonio Cortes
Journal Ethics and Malpractice
2017 NTAS Conference Selected Articles
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redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=\linewidth]{fig/dsfpn_framework.png}
	\end{center}
	\vspace{-0.3cm}
	\caption{ The diagram of the proposed dually supervised FPN~\cite{lin2017feature} (DSFPN). The green and blue pyramid represent bottom-up and top-down subnets, respectively.
The green dashed boxes are auxiliary prediction (\ie, detection or segmentation) heads. The black dashed box is the decoupled \textit{cls-reg} heads in detection task. Note that the prediction heads with the same color share weights.}
	\label{fig:dsfpn_framework}
\end{figure}

\begin{figure*}[t]
	\centering
	\begin{subfigure}[b]{0.095\linewidth}
		\includegraphics[width=\linewidth]{fig/single_head.png}
	\caption{}
		\label{fig:single_head}
	\end{subfigure}
	~
	\begin{subfigure}[b]{0.12\linewidth}
		\includegraphics[width=\linewidth]{fig/multi_head.png}
			\caption{}

		\label{fig:multi_head}
	\end{subfigure}
	~
	\begin{subfigure}[b]{0.23\linewidth}
		\includegraphics[width=\linewidth]{fig/u_single_head.png}
			\caption{}
		\label{fig:u_single_head}
	\end{subfigure}
	~
	\begin{subfigure}[b]{0.23\linewidth}
		\includegraphics[width=\linewidth]{fig/u_multi_head.png}
			\caption{}

		\label{fig:u_multi_head}
	\end{subfigure}
	~
	\begin{subfigure}[b]{0.23\linewidth}
		\includegraphics[width=\linewidth]{fig/dsfpn.png}
			\caption{}

		\label{fig:dsfpn}
	\end{subfigure}
	\caption{(a-c) Typical architectures in object classification, detection, and segmentation. (d) The proposed DSFPN. }
	\label{fig:typical_architecture}
\end{figure*}

As one of the most fundamental problems in computer vision, object detection has been studied for decades. Following the rise of deep learning in computer vision, object detection has been greatly advanced in recent years. Numerous deep detectors are proposed and achieve promising results on the large-scale benchmark MS COCO~\cite{lin2014microsoft}. In general, these deep detectors can be categorized into two types: one-stage model~\cite{liu2016ssd,redmon2016you,redmon2017yolo9000,zhang2018single,law2018cornernet,duan2019centernet,yang2019reppoints} and two-stage model~\cite{girshick2014rich,girshick2015fast,ren2015faster,dai2016r,he2017mask,cai2018cascade}. Compared with one-stage approaches, two-stage detection models usually consist of three components: {\it backbone network}, {\it region proposal head} and {\it detection head}. Given object proposals, a feature pyramid architecture (\eg, FPN~\cite{lin2017feature}) is often adopted as the backbone network in the two-stage detectors. After that, a detection head is  attached to the backbone in a lateral connect manner for achieving object localization and classification, as shown in the blue box in Fig.~\ref{fig:dsfpn_framework}. As a natural extension, this framework has also been widely utilized for instance segmentation by adding segmentation heads.

Despite excellent performance obtained by this pyramid architecture, its potential has not been fully explored, owing to the insufficient utilization of supervision information. The reasons of the insufficient utilization can be summarized into two aspects: gradient degradation in feature pyramid and task inconsistency in detection head. In particular,
the feature pyramid architecture is composited by two components: bottom-up subnet and top-down subnet; the prediction heads are mounted on top-down path (see Fig.~\ref{fig:dsfpn_framework}). This may lead to the inadequate training of bottom-up subnet due to the presence of gradients degradation~\cite{he2016deep}.
Consequently, the top-down feature pyramid, which is composed of features from bottom-up counterpart, has shortage of representation capability. Further, the detection and segmentation performance are not saturated under feature pyramid architecture.

The task inconsistency is an inherent problem~\cite{cheng2018revisiting} in current structure of the detection head, which is constructed by two weights shared sibling subnets for classification and regression, respectively. This structure limits the ability of the head to utilize supervision information.
Specifically, the sibling subnets deal with the two {\it inconsistent} tasks (classification and regression) with shared FC layers, resulting in sub-optimal solutions for each one~\cite{cheng2018revisiting}. Thus, in feature space the tangled supervision information in the two tasks cannot be effectively leveraged to facilitate the feature learning in the feature pyramid. As a consequence, the detection performance is seriously under-explored.

 Addressing the aforementioned issues, we first propose a novel supervised learning strategy for feature pyramid network, called \textit{dually supervised feature pyramid network} (DSFPN), which is embodied by mounting extra prediction heads (\ie, detection or segmentation) on the bottom-up layers through lateral connection. This design strategy encourages the supervision signal to directly propagate to the bottom-up feature pyramid, so as to strengthen supervision information for the feature learning in it. Moreover, the prediction heads on the bottom-up part can serve as a regularization term to promote the model training. In the test phase, the auxiliary prediction heads are discarded, thus no extra computational cost is needed for model inference.
In DSFPN, the \textit{cls} and \textit{reg} subnets (black dashed box of Fig.~\ref{fig:dsfpn_framework}) are decoupled to enhance the capacity of detection head to handle the task inconsistency, \ie, classification vs regression. Specifically, the decoupling increases the capacity of the heads such that the supervision information can be fully exploited to certain extent to strengthen the guidance of feature learning in the pyramid architecture.

The proposed \textit{dual supervision} (DS) and \textit{decouple} (DC) operations are general to a series of feature pyramid based detectors. The two operations are evaluated by incorporating into four representative FPN-based detectors (Faster RCNN~\cite{ren2015faster}, Mask RCNN~\cite{he2017mask}, Cascade RCNN~\cite{cai2018cascade}, and Cascade Mask RCNN~\cite{cai2018cascade}) with various backbone networks (ResNet-50/101~\cite{he2016deep}, ResNeXt-101~\cite{xie2017aggregated}) on the MS COCO dataset~\cite{lin2014microsoft}.

In summary, the paper makes the following main contributions:
\begin{enumerate}
	\item A novel DSFPN is proposed to solve the gradient degradation problem in FPN~\cite{lin2017feature} for object detection and segmentation, so as to facilitate feature learning, promote model training. The task inconsistency in detection head is alleviated by decoupling the \textit{cls} and \textit{reg} subnets.

	\item The proposed method consistently improves Faster RCNN~\cite{ren2015faster}, Mask RCNN~\cite{he2017mask}, Cascade RCNN~\cite{cai2018cascade}, and Cascade Mask RCNN~\cite{cai2019cascade} by a promising margin and achieves the state-of-the-art performance on COCO~\cite{lin2014microsoft} with negligible extra computational cost.
\end{enumerate}

The rest of the paper is organized as follows. Section~\ref{relatedwork} briefly reviews the relevant works. The proposed method is described in detail in Section~\ref{method}. Section~\ref{experiment} presents the experimental results. Section~\ref{conclusion} draws conclusions.

\begin{figure*}[t]
	\centering
	\begin{subfigure}[b]{0.4\linewidth}
		\includegraphics[width=\linewidth]{fig/faster_mask_w_dsfpn.png}
		\caption{Faster/Mask RCNN w DSFPN}
		\label{fig:faster_mask_w_dsfpn}
	\end{subfigure}
	\hfill
	\begin{subfigure}[b]{0.5\linewidth}
		\includegraphics[width=\linewidth]{fig/cascade_mask_w_dsfpn.png}
		\caption{Cascade/Cascade Mask RCNN w DSFPN}
		\label{fig:cascade_mask_w_dsfpn}
	\end{subfigure}
	\caption{The illustration of Faster/Mask/Cascade/CascadeMask RCNN with dually supervised FPN~\cite{lin2017feature} (DSFPN). ``B", ``D", and ``S" indicate proposed \textit{box}, \textit{detection}, and \textit{segmentation} heads, respectively. ``P" is the short for RoI \textit{pooling} operation. ``I" represents the input image. Green and blue pyramid are bottom-up and top-down subnets. The heads mounted on bottom-up pyramid are auxiliary ones.}
	\label{fig:detectors_w_dsfpn}
\end{figure*}

\section{Related work}
\label{relatedwork}
Object detection has been studied for decades, and numerous excellent works have been proposed. In this section, we will review the most relevant of them to highlight the differences of our work.

\noindent
{\bf Two-stage methods.}
The two-stage methods divide the detection into two steps: object proposals and object detection. In general, candidate regions are first yielded; then a detection module classifies these regions and refines the region location. Fast~\cite{girshick2015fast}/Faster RCNN~\cite{ren2015faster} mount a detection head on the top level of deep convolution networks (ConvNets) to take advantage of the rich feature representation for object detection (Fig.~\ref{fig:single_head}). However, this manner neglects the hierarchical characteristics of the deep ConvNets. To utilize this characteristics,  MS-CNN~\cite{cai2016unified} proposes to predict object at multiple levels of deep ConvNets (Fig.~\ref{fig:multi_head}). Although this modification promotes the detectors ability to deal with multi-scales of objects, the low level features lack semantic information for object classification. FPN~\cite{lin2017feature} significantly alleviates this problem by presenting a novel feature pyramid architecture, where low level features are strengthened by a top-down feature pyramid network and object prediction are conducted on each level via lateral connection (Fig.~\ref{fig:u_multi_head}). Further, Mask RCNN~\cite{he2017mask} unifies object detection and segmentation into an end-to-end framework on the basis of FPN~\cite{lin2017feature}.
More recently, based on FPN~\cite{lin2017feature}, Cascade RCNN and Cascade Mask RCNN~\cite{cai2019cascade,cai2018cascade} propose to yield high quality object detection and segmentation through multi-stage regression.
Despite superior performance, the feature learning in bottom-up pyramid is not fully explored.

\noindent
{\bf One-stage methods.}
In contrast to two-stage methods, one-stage methods directly perform object detection without proposals, e.g., SSD~\cite{liu2016ssd} and YOLO~\cite{redmon2016you}. Same with Faster RCNN~\cite{ren2015faster}, YOLO~\cite{redmon2016you} predicts objects on the top level of deep ConvNets. Thus, the hierarchy is ignored in detection.
Similar with MS-CNN~\cite{cai2016unified}, SSD~\cite{liu2016ssd} assigns detection for different scales of objects on different levels of deep ConvNets, which separates the multi-scale objects in the feature space. DSSD~\cite{fu2017dssd} improves SSD~\cite{liu2016ssd} performance by introducing additional context via deconvolution. Further, RefineDet~\cite{zhang2018single} boosts DSSD~\cite{fu2017dssd} using the bottom-up feature pyramid for proposal and the top-down counterpart for detection. However, the major performance gain is from the two-stage box regression but not the bottom-up feature pyramid learning. Moreover, the prediction on bottom-up pyramid is involved in both training and test phase.

\noindent
{\bf Supervision in deep ConvNets.}
Usually, the feature learning in deep ConvNets is guided by a supervision signal from the output layer, e.g., VGG~\cite{simonyan2014very}, ResNet~\cite{he2016deep} (Fig.~\ref{fig:single_head}), and UNet~\cite{ronneberger2015u} (Fig.~\ref{fig:u_single_head}). This results in the gradient vanishing with the increasing of depth of layers, such that the network is hard to effectively train. While ResNet~\cite{he2016deep} alleviates the the problem via skip connection, it still remains a challenge to train deep ConvNets. DSN~\cite{lee2015deeply} solves this problem in general classification task by proposing a deeply-supervised training architecture, where auxiliary classifiers are introduced to hidden layers (Fig.~\ref{fig:multi_head}). PSPNet~\cite{zhao2017pyramid} demonstrates that adding an extra loss at the relatively high level layer of deep ConvNets can benefit the performance improvement in scene parsing.
The similar idea is also exploited in HED~\cite{xie2015holistically} for edge detection, but the prediction heads are attached on each level of deep ConvNets. Additionally, in HED~\cite{xie2015holistically} all the heads are needed in inference phase.

\noindent
{\bf Difference from previous works.}
To highlight the differences of proposed dually supervised FPN (DSFPN) from previous related works, we summarize the typical network architecture for object classification, detection and segmentation in Fig.~\ref{fig:typical_architecture}. For better illustration, the skip connection is omitted in the backbone network.
By comparing with Fig.~\ref{fig:single_head} and \ref{fig:u_single_head}, where the supervision signal is simply at the final output layer, DSFPN has multiple supervision signals on every layer of the network. Although in Fig.~\ref{fig:multi_head} the supervision signals are also on each layer, this structure is not suited for object detection or segmentation, due to the shortage of representation ability in lower layers. Note that SSD~\cite{liu2016ssd} adopts a similar architecture to Fig.~\ref{fig:multi_head} but uses relatively higher layers. In contrast to Fig.~\ref{fig:u_multi_head}, DSFPN (Fig.~\ref{fig:dsfpn}) adds auxiliary supervision signals on inner layers, such that the network can be trained more effectively.

\section{Method}
\label{method}

In this section we elaborate the proposed \textit{dually supervised FPN} (DSFPN) in details and present its formulation.
\subsection{Overview}
Generally speaking, DSFPN is proposed to deal with the gradient degradation in feature pyramid network for object detection or segmentation at region level during training phase. To this end, when proposals are given, auxiliary prediction heads (\ie, object detection or segmentation heads) are attached to the bottom-up subnet (Fig.~\ref{fig:detectors_w_dsfpn}), such that supervision signal can immediately direct the feature learning in it. In the test phase, these auxiliary heads are discarded so that no extra computation is needed in model inference.
Formally, given a set of $N$ proposal boxes $B=\{b_i| i=1, 2, ..., N\}$, they are projected to a level of feature layer according to the box size. Here we use the $k$-th level as an example, the boxes on the $k$-th level are $B_k$. The feature maps of the $k$-th level are denoted as $f^k$. Thus, the detection and segmentation heads on top-down pyramid can be formulated as $D_{td}^k(f_{td}^k,B_k)$ and $S_{td}^k(f_{td}^k,B_k)$, respectively. Accordingly, the prediction heads on bottom-up pyramid are $D_{bu}^k(f_{bu}^k,B_k)$ and $S_{bu}^k(f_{bu}^k,B_k)$.
In the training phase, the predictions over the both pyramid subnets are $D=\{D_{td}^k, D_{bu}^k|k=1, 2, ..., M\}$ and $S=\{S_{td}^k, S_{bu}^k|k=1, 2, ..., M\}$, where $M$ is the number of levels of pyramid. Here, `td' and `bu' indicate top-down and bottom-up, respectively. In the inference phase, the predictions are $D=\{D_{td}^k|k=1, 2, ..., M\}$ and $S=\{S_{td}^k|k=1, 2, ..., M\}$, since $D_{bu}^k$ and $S_{bu}^k$ are removed.
The given proposal boxes are not necessary from proposal stage (\eg, RPN~\cite{ren2015faster}), they can also come from latter stages. The effect of choosing the boxes from different stages will be discussed in Experiment. Since the object or segmentation heads over different levels share same weights, the $k$ in each head notation is dropped for simplicity in subsequent sections.

\subsection{Dual supervision}
The proposed DSFPN is general to two- and multi-stage detectors, we describe the concrete architecture one by one.

\begin{figure}[t]
	\begin{center}
	\includegraphics[width=1\linewidth]{fig/cls_reg_decouple.png}
	\end{center}
	\caption{The structure of detection head in FPN~\cite{lin2017feature} (left) and the proposed DSFPN (right).}
	\label{fig:cls_reg_decouple}
\end{figure}

\noindent
{\bf Two-stage detector.}
In two-stage detectors, we use two representative methods, Faster RCNN~\cite{ren2015faster} and Mask RCNN~\cite{he2017mask}, as examples to illustrate the process of incorporating  dual supervision (DS). As shown in Fig.~\ref{fig:faster_mask_w_dsfpn}, the proposal boxes, $B0$, are first given and projected to each level of pyramid. In Faster/Mask RCNN~\cite{ren2015faster,he2017mask}, $B0$ is yielded by RPN~\cite{ren2015faster}, which is not displayed in Fig.~\ref{fig:faster_mask_w_dsfpn}. The detection and segmentation over both top-down and bottom-up pyramids are $D1$, $S1$, $D0$, and $S0$, which correspond to $D_{td}$, $S_{td}$, $D_{bu}$, and $S_{bu}$, respectively. The final loss of the detector can be written as
\begin{equation}
\label{eq1}
\begin{aligned}
\mathcal{L}_{final}=\alpha_1\mathcal{L}(D_{gt},D0)+\alpha_2\mathcal{L}(D_{gt},D1)\\
+\alpha_3\mathcal{L}(S_{gt},S0)+\alpha_4\mathcal{L}(S_{gt},S1)
\end{aligned}
\end{equation}
where the $D_{gt}$ and $S_{gt}$ represent ground truth of detection and segmentation; $\mathcal{L}(D,\cdot)$ and $\mathcal{L}(S,\cdot)$ are the detection and segmentation loss functions; $\alpha_1, \alpha_2, \alpha_3, \alpha_4$ are the weights for each loss. The specific formulas of the two functions are referred to~\cite{he2017mask}. The $\mathcal{L}(S,\cdot)$ can be omitted when segmentation ground truth is not available.

\noindent
{\bf Multi-stage detector.}
Recently proposed Cascade and Cascade Mask RCNN~\cite{cai2018cascade,cai2019cascade} are used to demonstrate how to integrate DS into multi-stage detectors (Fig.~\ref{fig:cascade_mask_w_dsfpn}).
The proposed boxes can be produced from RPN~\cite{ren2015faster}, detection head at first stage ($D1$) or second stage ($D2$), which are denoted as $B0$, $B1$, and $B2$, respectively. On top-down pyramid, the proposed boxes can only be $B0$.
However, on bottom-up pyramid, the boxes can be one of the $B0, B1$, and $B2$.
Similar to two-stage detectors, the predictions over bottom-up pyramid are $D0$ and $S0$. However, the predictions over top-down pyramid are separated into three sets: $D1$, $D2$, and $D3$. As stated in Cascade Mask RCNN~\cite{cai2019cascade}, the segmentation head is simply attached at stage three (denoted as $S3$) for the trade-off between accuracy and computation complexity.
Thus, the final loss of the detectors is written as
\begin{equation}
\label{eq2}
\begin{aligned}
\mathcal{L}_{final}=&\alpha_1\mathcal{L}(D_{gt},D0)+\alpha_2\mathcal{L}(S_{gt},S0)\\
 &+\alpha_3\mathcal{L}(S3_{gt},S3)+
\sum_{i=1}^{T}\alpha_{si}\mathcal{L}(Di_{gt},Di)
\end{aligned}
\end{equation}
where $i$ is the index of the prediction stage on the top-down pyramid; $T$ is the total number of stages;  $S3$ and $S3_{gt}$ are the segmentation predictions at stage three and its corresponding ground truth; $Di$ and $Di_{gt}$ are detections and  ground truth at stage $i$; $\mathcal{L}(D,\cdot)$ and $\mathcal{L}(S,\cdot)$ are the detection and segmentation loss functions; $\alpha_1, \alpha_2, \alpha_3, \alpha_{si}$ are weights of each prediction head. Segmentation related terms only exist in Cascade Mask~RCNN~\cite{cai2019cascade}.
\vspace{-0cm}
\begin{table*}[t]
	\centering
	\footnotesize
	\caption{The \textbf{ablation study} in representative \textbf{two-stage detectors}, \ie, Faster RCNN and Mask RCNN. $1\times$ and $2\times$ indicate 12 epochs and 24 epochs of training schedule, respectively. $\boldsymbol{\star}$ indicates multi-scale testing.}
	\begin{tabular}{c|c|c|c|cccccc|cccccc}
		\hline
		\hline
		Method&backbone&DS&DC&AP$_{bbox}$&AP$_{50}$&AP$_{75}$&AP$_S$&AP$_M$&AP$_L$&AP$_{mask}$&AP$_{50}$&AP$_{75}$\\
		\hline
	    \multirow{4}{*}{Faster RCNN$_{1\times}$}&\multirow{4}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&36.7&58.4&39.6&21.1&39.9&48.1&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{55}}&37.7&59.0&40.6&20.6&40.6&40.6&-&-&-\\

	    &&\text{\ding{55}}&\text{\ding{51}}&37.5&58.8&40.8&21.1&40.7&49.2&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{38.0}&\textbf{59.3}&\textbf{41.3}&\textbf{21.3}&\textbf{40.9}&\textbf{49.7}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Faster RCNN $_{2\times}$}&\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&37.9&59.3&41.1&21.5&41.1&49.9&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{39.2}&\textbf{60.1}&\textbf{42.7}&\textbf{22.2}&\textbf{42.3}&\textbf{52.1}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Faster RCNN $_{1\times}$}&\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&39.4&61.2&43.4&22.6&42.9&51.4&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{40.0}&61.2&43.4&22.6&\textbf{43.3}&\textbf{53.3}&-&-&-\\
        \hline
	    \multirow{2}{*}{Faster RCNN $_{2\times}$}&\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&39.8&61.3&43.3&22.9&43.3&52.6&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{41.1}&\textbf{62.2}&\textbf{44.3}&\textbf{23.2}&\textbf{44.4}&\textbf{54.8}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Faster RCNN$_{1\times}$} &\multirow{2}{*}{ResNeXt101}&\text{\ding{55}}&\text{\ding{55}}&41.5&63.8&44.9&24.8&45.4&53.5&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.5}&63.8&\textbf{46.3}&\textbf{25.2}&\textbf{46.4}&\textbf{55.7}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Faster RCNN$_{2\times}$} &\multirow{2}{*}{ResNeXt101}&\text{\ding{55}}&\text{\ding{55}}&40.8&62.5&44.4&23.4&44.3&53.8&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.6}&\textbf{63.5}&\textbf{46.4}&\textbf{24.7}&\textbf{46.5}&\textbf{56.0}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Faster RCNN$_{2\times}$$\boldsymbol{\star}$} &\multirow{2}{*}{ResNeXt101}&\text{\ding{55}}&\text{\ding{55}}&44.0&65.0&47.9&28.7&46.9&57.4&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{45.4}&\textbf{65.4}&\textbf{49.2}&\textbf{29.4}&\textbf{48.5}&\textbf{58.9}&-&-&-\\

	    \hline
	    \hline
	    \multirow{2}{*}{Mask RCNN$_{1\times}$} &\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&37.7&59.2&40.9&21.4&40.8&49.8&33.9&55.8&35.8\\

	     &&\text{\ding{51}}&\text{\ding{51}}&\textbf{38.8}&\textbf{59.6}&\textbf{42.3}&\textbf{21.5}&\textbf{41.6}&\textbf{51.7}&\textbf{34.6}&\textbf{56.0}&\textbf{37.0}\\
	    \hline
	    \multirow{2}{*}{Mask RCNN $_{2\times}$}&\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&38.6&59.8&42.1&22.2&41.5&50.8&34.5&56.5&36.3\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{40.0}&\textbf{60.9}&\textbf{43.1}&\textbf{22.4}&\textbf{42.6}&\textbf{54.1}&\textbf{35.6}&\textbf{57.6}&\textbf{37.7}\\
	    \hline
	    \multirow{2}{*}{Mask RCNN$_{1\times}$} &\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&40.0&61.8&43.7&22.5&43.4&52.7&35.9&58.3&38.0\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{40.7}&\textbf{61.5}&\textbf{44.6}&\textbf{22.5}&\textbf{43.8}&\textbf{54.5}&\textbf{36.3}&\textbf{58.3}&\textbf{38.5}\\
	    \hline
	    \multirow{2}{*}{Mask RCNN$_{2\times}$} &\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&40.9&61.9&44.8&23.5&44.2&53.9&36.4&58.3&38.7\\
	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.1}&\textbf{62.6}&\textbf{45.8}&\textbf{23.9}&\textbf{45.3}&\textbf{56.1}&\textbf{37.1}&\textbf{59.2}&\textbf{39.5}\\
	    \hline
	    \multirow{2}{*}{Mask RCNN$_{1\times}$} &\multirow{2}{*}{ResNeXt101}&\text{\ding{55}}&\text{\ding{55}}&42.4&64.3&46.5&25.5&46.7&54.6&37.5&60.6&39.9\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.8}&63.5&\textbf{46.7}&\textbf{25.0}&\textbf{46.5}&\textbf{56.3}&\textbf{37.6}&60.1&39.9\\
	    \hline
	    \multirow{2}{*}{Mask RCNN$_{2\times}$} &\multirow{2}{*}{ResNeXt101}&\text{\ding{55}}&\text{\ding{55}}&42.3&63.6&45.9&25.4&45.9&55.7&37.2&59.9&39.4\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{43.3}&\textbf{63.9}&\textbf{47.1}&\textbf{25.9}&\textbf{46.8}&\textbf{57.0}&\textbf{37.9}&\textbf{60.4}&\textbf{40.4}\\
	    \hline
	\end{tabular}
	\label{table:results_faster_mask_rcnn}
\end{table*}

\subsection{Decoupled head}
For detection task, the architecture of the \textit{cls}-\textit{reg} decoupled head is shown in Fig.~\ref{fig:cls_reg_decouple} right.  Comparing with the original detection head structure (Fig.~\ref{fig:cls_reg_decouple} left), the decoupled head separates the classification and regression tasks in hidden feature space, which is achieved by taking apart the shared two hidden layers with 1,024 nodes. Formally, the detection head is written as
\begin{equation}
\label{eq3}
\begin{aligned}
D(B,f)&=C(B,f)+R(B,f)\\
C(B,f)&=W_{c}^2\cdot W_{c}^1\cdot f^B\\
R(B,f)&=W_{r}^2\cdot W_{r}^1\cdot f^B
\end{aligned}
\end{equation}
 where $C$ and $R$ symbolize classification and regression, respectively; $W_c$ and $W_r$ represent the weights of hidden layers of classification and regression, respectively; `1' and `2' denote indices of hidden layers; $f^B$ is the feature in box regions.
This simple operation increases the capacity of detection head to address two inconsistent tasks. Moreover, the decoupling disentangles the propagation of supervision signal until the RoI pooling.

\begin{table*}[t]
\centering
	\footnotesize
	\caption{The \textbf{ablation study} in representative \textbf{multi-stage detectors}, \ie, Cascade RCNN and Cascade Mask RCNN. $1\times$ and $2\times$ indicate 12 epochs and 24 epochs of training schedule, respectively.}
	\begin{tabular}{c|c|c|c|cccccc|cccccc}
		\hline
		\hline
		Method&backbone&DS&DC&AP$_{bbox}$&AP$_{50}$&AP$_{75}$&AP$_S$&AP$_M$&AP$_L$&AP$_{mask}$&AP$_{50}$&AP$_{75}$\\
		\hline
	    \multirow{2}{*}{Cascade RCNN $_{1\times}$}&\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&40.9&59.0&44.6&22.5&43.6&55.3&-&-&-\\
	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{41.3}&\textbf{59.6}&\textbf{44.9}&\textbf{22.6}&\textbf{44.1}&\textbf{55.8}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Cascade RCNN $_{2\times}$}&\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&41.3&59.7&44.9&23.8&44.0&55.3&-&-&-\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.1}&\textbf{60.5}&\textbf{45.6}&\textbf{23.5}&\textbf{44.8}&\textbf{56.9}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Cascade RCNN$_{1\times}$} &\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&42.8&61.4&46.8&24.1&45.8&57.4&-&-&-\\
	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{43.1}&\textbf{61.7}&\textbf{47.0}&\textbf{24.4}&\textbf{46.5}&\textbf{57.8}&-&-&-\\
	    \hline
	    \multirow{2}{*}{Cascade RCNN $_{2\times}$}&\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&43.0&61.4&46.9&23.9&46.1&58.0&-&-&-\\

        &&\text{\ding{51}}&\text{\ding{51}}&\textbf{43.7}&\textbf{62.3}&\textbf{47.5}&\textbf{24.7}&\textbf{47.0}&\textbf{58.8}&-&-&-\\
	    \hline
	    \hline
	    \multirow{2}{*}{CascadeMask RCNN $_{1\times}$}&\multirow{2}{*}{ResNet50}&\text{\ding{55}}&\text{\ding{55}}&41.3&59.6&44.9&23.1&44.2&55.4&35.4&56.2&37.8\\
	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{42.0}&\textbf{60.1}&\textbf{45.8}&\textbf{23.4}&\textbf{45.1}&\textbf{56.6}&\textbf{35.9}&\textbf{56.7}&\textbf{38.4}\\
	    \hline

	    \multirow{2}{*}{CascadeMask RCNN $_{1\times}$}&\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&43.3&61.7&47.2&24.2&46.3&58.2&37.1&58.6&39.8\\
	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{43.6}&\textbf{61.9}&\textbf{47.4}&\textbf{24.6}&\textbf{46.8}&\textbf{58.5}&\textbf{37.3}&58.5&\textbf{40.0}\\
	    \hline
	    \multirow{2}{*}{CascadeMask RCNN $_{2\times}$}&\multirow{2}{*}{ResNet101}&\text{\ding{55}}&\text{\ding{55}}&43.5&62.1&47.2&24.3&46.9&58.2&37.2&58.7&39.5\\

	    &&\text{\ding{51}}&\text{\ding{51}}&\textbf{44.5}&\textbf{63.0}&\textbf{48.4}&\textbf{24.9}&\textbf{47.8}&\textbf{60.0}&\textbf{38.1}&\textbf{59.6}&\textbf{41.0}\\
	    \hline
	\end{tabular}
	\label{table:results_cascade_mask_rcnn}
\end{table*}

\section{Experiments}
\label{experiment}
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=0.83\linewidth,height=.55\linewidth]{fig/APbbox_improvement.pdf}
	\end{center}
	\vspace{-2mm}
	\caption{The effect of DS+DC on performance change from $2\times$ setting. The detector is Faster RCNN.}
	\label{fig:APbbox_improvement}
\end{figure}
\subsection{Implementation details}
We perform experiments on the MS COCO dataset~\cite{lin2014microsoft} with 80 categories. The union of 80k train images and 35k images of val images (train2017) is used as train dataset. The 5k of val (val2017) is utilized to report results of ablation study. The final results are reported on the test-dev dataset for fair comparison with state-of-the-art detectors.
We implement our dually supervised FPN (DSFPN) based on the detetron\footnote{https://github.com/facebookresearch/Detectron} library and Caffe2\footnote{https://caffe2.ai/} framework. By convention, we set the short side of an input image to 800 pixels and the other side is less than $1,333$ pixels. The number of anchors and proposals are set as default. The RoIAlign in~\cite{he2017mask} is adopted in all experiments. The backbone network of all detectors includes FPN~\cite{lin2017feature}. For Faster/Mssk/Cascade RCNN based on ResNet50/ResNet101~\cite{he2016deep}, synchronized SGD is used to train the model on 8 P100 GPUs, each mini-batch involves 2 images per GPU. The other detectors are trained with 1 image per GPU, due to the limited GPU memory. No data augmentation is used except for horizontal flipping. Both $1\times$ (\ie, 12 epochs) and $2\times$ (\ie, 24 epochs) training schedules are utilized in experiments. The weights of each item of final loss are set to 1. In inference, all experiments are conducted on a single image scale ($800\times1,333$ pixels) without bells and whistles, unless otherwise noted.

\subsection{Ablation study}

We validate the effects of dual supervision (DS) and decouple (DC) operation on  Faster/Mask RCNN~\cite{ren2015faster,he2017mask} and Cascade/CascadeMask RCNN~\cite{cai2018cascade,cai2019cascade}. The generalizability of the proposed DSFPN is evaluated by comparing experimental results on the val2017 and test-dev datasets.  The inference time is measured on a 1080Ti GPU with input size $800\times1,333$ pixels.

\noindent
{\bf Effects of DS and DC on Faster/Mask RCNN.}
From Table~\ref{table:results_faster_mask_rcnn}, we note that DS and DC improve the baseline, Faster RCNN$_{1\times}$ with ResNet50~\cite{he2016deep}, by 1 and 0.8 points in AP$_{bbox}$, respectively. Combining them together, the improvement in AP$_{bbox}$ reaches 1.3 points. By conducting experiments over various baseline, backbone, and training schedule, we find that the  increment is consistent in terms of AP$_{bbox}$ over different IoUs and scales. An interesting observation is that in baselines, the deeper the backbone, the less the effect of doubling training schedule ($2\times$) (Fig.~\ref{fig:APbbox_improvement}). For example, when backbone is ResNet50 and detector is Faster RCNN, the $2\times$ setting increases the AP$_{bbox}$ by $1.2$ points; when backbone is ResNet101, the incremental margin declines to $0.4$ points; when backbone is ResNeXt101, the incremental magnitude even becomes a negative value ($-0.7$ points).
This is because the deeper the network, the harder the training.
In contrast, when the baselines are equipped with DS and DC, the $2\times$ setting consistently improves AP$_{bbox}$ over all backbones.
This can be attributed to the promotion of model training via enhanced supervision signal.  In addition to detection, Mask RCNN with DS and DC also outperforms all the ones without the two operations in terms of AP$_{mask}$, and the effect on $2\times$ is apparent as well.

\noindent
{\bf Effects of DS and DC on Cascade/CascadeMask RCNN.}
As shown in Table~\ref{table:results_cascade_mask_rcnn}, promising improvement of AP$_{bbox}$ is gained in all baseline detectors by adding DS and DC. Similar to the two-stage detectors, when DS and DC are involved in model, detectors can benefit more from the double training schedule ($2\times$), especially for deeper backbones. Specifically, when backbone network is ResNet101 and detector is Cascade RCNN, $2\times$ helps the model with DS and DC to obtain a higher improvement margin than that without DS and DC ($43.7$ vs $43.1$ and $43.0$ vs $42.8$).  By comparing the performance in terms of AP$_{50}$ and AP$_{75}$ in detection task, we see that adding DS and DC can achieve larger increment in AP$_{50}$ than that in AP$_{75}$. This can be attributed to the multi-stage regression yielding high-quality box, such that little room remains to explore in this direction. In the segmentation task, adding DP and DC can still lead to performance improvement, even if the backbone is ResNet101.
\begin{table}[t]
	\centering
	\footnotesize
	\caption{The effect of boxes in the bottom-up pyramid.
	\begin{tabular}{c|c|c|ccc}
		\hline
		\hline
		&backbone&stage&AP& AP$_{50}$&AP$_{75}$\\
		\hline
	    Cascade RCNN $1\times$ &ResNet50&0&41.3&59.6&44.9\\
	    \hline
	    Cascade RCNN $1\times$ &ResNet50&1&41.0&59.2&44.3\\
	    \hline
	    Cascade RCNN $1\times$ &ResNet50&2&40.9&59.0&44.1\\
	    \hline
	\end{tabular}
	\label{table:effect_box_stage012}
\end{table}

\begin{table*}[t]
\centering
\caption{Generalizability of the proposed DSFPN.}
\resizebox{1\textwidth}{!}
\begin{tabular}{c|c|c|c|cccccc|cccccc}
\hline
\hline
\multirow{2}{*}{method} & \multirow{2}{*}{backbone} & \multirow{2}{*}{DSFPN} & \multirow{2}{*}{test speed} & \multicolumn{6}{c|}{val2017} & \multicolumn{6}{c}{test-dev} \\ \cline{5-16}
 &  &  &  & $AP_{bbox}$ & $AP_{50}$ & $AP_{75}$ & $AP_{mask}$ & $AP_{50}$ & $AP_{75}$ & $AP_{bbox}$ & $AP_{50}$ &$ AP_{75}$ &$AP_{mask}$  & $AP_{50}$ & $AP_{75}$ \\
\hline
\multirow{2}{*}{FasterRCNN$_{2\times}$} & \multirow{2}{*}{ResNet50} &  \text{\ding{55}}&  0.074s&37.9  &59.3  &41.1  &-  &-  &-  &37.9  &59.6  &41.0  &-  &-  &-  \\

 & & \text{\ding{51}} &0.075s& 39.2 & 60.1 & 42.7 &-  &-  &-  &39.3  &60.5  &42.8  &-  &-  &- \\
 \hline
 \multirow{2}{*}{FasterRCNN$_{2\times}$} & \multirow{2}{*}{ResNet101} &  \text{\ding{55}}& 0.097s &39.8  & 61.3 &43.3  & - & - & - & 40.0 & 61.7 & 43.5 & - & - & - \\

 &  & \text{\ding{51}} &0.098s  & 41.1 & 62.2&44.3&-  &-  &-  &41.3  &62.5  &44.5&-  &-  &- \\
 \hline
 \multirow{2}{*}{FasterRCNN$_{2\times}$} & \multirow{2}{*}{ResNeXt101} &  \text{\ding{55}}&  0.202s&40.8  &62.5  &44.4  &-  & - & - &41.1  &63.1  &44.6  & - & - & - \\

 &  &\text{\ding{51}}  &  0.203s&42.6  &63.5  &46.4  & - &-  &-  &43.0  &64.4  &46.7  &-  &-  &- \\
 \hline
 \multirow{2}{*}{MaskRCNN$_{2\times}$} & \multirow{2}{*}{ResNet50} &  \text{\ding{55}}&  0.082s&38.6  &59.8  &42.1  &34.5  &56.5  &36.3  &39.0  &60.5  &42.6  &34.9  &57.2  &36.9  \\

 &  & \text{\ding{51}} &  0.084s&40.0  &60.9  &43.1  &35.6  &57.6  &37.7  &40.2  &61.1  &43.7  &35.7  &57.8  &37.8 \\
 \hline
 \multirow{2}{*}{MaskRCNN$_{2\times}$} & \multirow{2}{*}{ResNet101} &  \text{\ding{55}}&  0.104s& 40.9 &61.9  & 44.8 & 36.4 & 58.5 & 38.7 & 41.0 &62.4  &44.8  &36.4  &59.1  &38.6  \\

 &  & \text{\ding{51}} & 0.105s &42.1  &62.6  & 45.8 &37.1  & 59.2 &39.5  &42.3  &63.0  &45.9  &37.3  &59.8  &39.6 \\
 \hline
 \multirow{2}{*}{MaskRCNN$_{2\times}$} & \multirow{2}{*}{ResNeXt101} &  \text{\ding{55}}&  0.210s&42.3  &63.6  &45.9  &37.2 &59.9  &39.4  &42.6  &64.1  &46.5  &37.6  &60.8  &39.9  \\

 &  & \text{\ding{51}} & 0.213s &43.3  &63.9  &47.1  &37.9  &60.4  &40.4  &43.8  &64.7  &47.9  &38.4  &61.5  &40.9 \\
 \hline
 \hline
 \multirow{2}{*}{CascadeRCNN$_{2\times}$} & \multirow{2}{*}{ResNet50} &  \text{\ding{55}}& 0.101s &41.3  &59.7  &44.9 &-  &-  &-  &41.5  &59.9  &45.2  &-  &-  &-  \\

 &  &  \text{\ding{51}}&  0.115s&42.1  &60.8  &45.6  & - & - &-  &42.1  &60.8  &45.7  &-  &-  &- \\
 \hline
 \multirow{2}{*}{CascadeRCNN$_{2\times}$} & \multirow{2}{*}{ResNet101} &  \text{\ding{55}}&  0.123s&  43.0& 61.4 & 46.9 &-  &-  &-  &43.0  &61.3  &47.0  &-  &-  &-  \\
 &  & \text{\ding{51}} &0.137s  &43.7  &62.3  &47.5  & - & - & - & 43.7 & 62.4 & 47.4 & - &-  &- \\
 \hline

 \multirow{2}{*}{CascadeMaskRCNN$_{2\times}$} & \multirow{2}{*}{ResNet101} & \text{\ding{55}} &0.131s&43.5  &62.1  &47.2  &37.2  &58.7  &39.5  &43.7  &62.3  &47.6  &37.4  &59.4  & 40.0 \\

 &  &\text{\ding{51}}  &0.147s  &44.5  &63.0  &48.4  &38.1  &59.6  &41.0  &44.5  &63.0  &48.3&38.0  &60.0  &40.8 \\
 \hline
\end{tabular}
}
\label{table:generalizability}
\end{table*}

\begin{figure*}[t]
	\begin{center}
	\includegraphics[width=1\linewidth]{fig/det_feat.png}
	\end{center}
	\vspace{-6mm}
	\caption{The feature map of each layers in FPN~\cite{lin2017feature} (left) and DSFPN (right). The green and blue boxes indicate that the feature maps enclosed are from bottom-up and top-down pyramids, respectively.}
	\label{fig:det_feat}
	\vspace{-3mm}
\end{figure*}

\begin{table*}[t]
	\centering
	\footnotesize
	\caption{Comparison with state-of-the-art methods on the COCO~\cite{lin2014microsoft} test-dev dataset.}
	\begin{tabular}{r|c|ccccccccc}
		\hline
		\hline
		method&backbone&AP&AP$_{50}$&AP$_{75}$&AP$_S$&AP$_M$&AP$_L$\\
	    \hline
	    YOLOv2~\cite{redmon2017yolo9000}&DarkNet-19&21.6&44.0&19.2&5.0&22.4&35.5\\
	    SSD-513~\cite{liu2016ssd} &ResNet101&31.2&50.4&33.3&10.2&34.5&49.8\\
	    DSSD-513~\cite{fu2017dssd} &ResNet101&33.2&53.3&35.2&13.0&35.4&51.1\\
	    RefineDet512~\cite{zhang2018single} &ResNet101&36.4&57.5&39.5&16.6&39.9&51.4\\
	    RetinaNet800~\cite{lin2017focal} &ResNet101&39.1&59.1&42.3&21.8&42.7&50.2\\
	    CornerNet~\cite{law2018cornernet} &Hourglass-104&40.5&56.5&43.1&19.4&42.7&53.9\\
	    FASF800~\cite{zhu2019feature}&ResNeXt101&42.9&63.8&46.3&26.6&46.2&52.7\\
	    \hline
	    Faster RCNN+++~\cite{he2016deep}&ResNet101&34.9&55.7&37.4&15.6&38.7&50.9\\
	    Faster RCNN w FPN~ \cite{lin2017feature}&ResNet101&36.2&59.1&39.0&18.2&39.0&48.2\\
	    Mask RCNN~\cite{he2017mask} &ResNeXt101&39.8&59.8&-&22.1&43.2&51.2\\
	    Grid RCNN~\cite{lu2019grid} w FPN&ResNet101&41.5&60.9&44.5&23.3&44.9&53.1\\
	    Grid RCNN~\cite{lu2019grid}w FPN&ResNeXt101&43.2&63.0&46.6&25.1&46.5&55.2\\
	    Libra RCNN~\cite{pang2019libra} w FPN&ResNet101&41.1&62.1&44.7&23.4&43.7&52.5\\
	    Libra RCNN~\cite{pang2019libra} w FPN&ResNeXt101&43.0&64.0&47.0&25.3&45.6&54.6\\
	    Cascade RCNN~\cite{cai2018cascade} w FPN&ResNet101&42.8&62.1&46.3&23.7&45.5&55.2\\
	    TridentNet~\cite{li2019scale}&ResNet101&42.7&63.6&46.5&23.9&46.6&56.6\\
	    Cascade RCNN w Rank-NMS~\cite{Tan_2019_ICCV}&ResNet101&43.2&61.8&47.0&24.6&46.2&55.4\\
	    \hline
	    Faster RCNN w DSFPN (ours) &ResNeXt101&43.0&64.4&46.7&25.3&46.1&54.7\\
	    Mask RCNN w DSFPN (ours) &ResNet101&42.3&63.0&45.9&23.4&44.9&54.6\\
	    Mask RCNN w DSFPN (ours) &ResNeXt101&43.8&\textbf{64.7}&47.9&\textbf{25.5}&46.8&55.7\\
	    Cascade RCNN w DSFPN (ours) &ResNet101&43.7&62.4&47.4&24.3&45.9&56.8\\
	    Cascade Mask RCNN w DSFPN (ours) &ResNet101&\textbf{44.5}&63.0&\textbf{48.3}&24.3&\textbf{47.2}&\textbf{58.5}\\
	    \hline

	\end{tabular}
	\label{table:compare_sota}
\end{table*}
\vspace{-0.5cm}
\noindent
{\bf Boxes generated from stage 0, 1, and 2.}
The experimental results with boxes from different stages are listed in Table~\ref{table:effect_box_stage012}. We note that (1) using boxes from stage 0 (\ie, RPN~\cite{ren2015faster}) achieves best performance; and (2) the higher the stage, the lower the performance. It suggests that when the boxes are coarse (\ie, boxes from stage 0), the auxiliary detection heads can better assist the model training. The reason may be that the coarse boxes contain a diversity of region samples, so that the auxiliary heads can facilitate the model training by taking advantage of the diversity.

\noindent
{\bf Generalizability of DSFPN.}
To demonstrate the generalizability of the proposed DSFPN, we conduct extensive experiments on val2017 and test-dev datasets. The experimental results are listed in Table~\ref{table:generalizability}. By comparing the results in val2017 and test-dev, we note that in two-stage methods, the performance increment gained by adding DSFPN is present in all backbones; moreover, the improvement is consistent across the val2017 and test-dev. This demonstrates that the proposed DSFPN promotes detection performance not by over-fitting to a validation data, it can generalize to test dataset.
In multi-stage methods, the consistent improvement also exists, but with subtle less margin. Apart from detection task, in the instance segmentation, both two and multi-stage methods with DSFPN surpass that  without DSFPN.  The aforementioned observations illustrate that the proposed DSFPN is general and robust to datasets and tasks.

\noindent
{\bf Inference time.}
From Table~\ref{table:generalizability}, we see that in two-stage methods, with or without DSFPN, the inference speed is almost the same; in multi-stage methods, the speed has a trivial difference. The subtle change in speed demonstrates that our DSFPN strengthens detectors precision with little extra computational cost.

\noindent
{\bf Visualization of feature maps.}
To verify that DSFPN does facilitate the feature learning in bottom-up pyramid and enhances the feature representation capability of top-down pyramid, we visualize the feature maps from each level of the pyramids in Fig.~\ref{fig:det_feat}. The displayed feature maps are the summation of the raw feature maps over channel at each pyramid level.
We can note that the feature maps from each level of bottom-up pyramid of DSFPN (green boxes in right of Fig.~\ref{fig:det_feat}) are cleaner and with less noise than that from FPN~\cite{lin2017feature} (green boxes left of Fig.~\ref{fig:det_feat}). Accordingly, the feature maps from top-down pyramid (blue boxes in right of Fig.~\ref{fig:det_feat}) also have stronger activation, comparing with the counterpart (blue boxes in left of Fig.~\ref{fig:det_feat}). Especially, at lower levels, the feature maps from DSFPN are significantly superior to that from FPN~\cite{lin2017feature}.
These observations experimentally demonstrate that the proposed DSFPN guides feature learning more effectively than FPN~\cite{lin2017feature} does.

\vspace{-0.2cm}
\subsection{Comparison with state-of-the-arts}
To demonstrate the effectiveness of the  proposed DSFPN, it is compared with representative and recently proposed methods on the COCO test-dev dataset. The results are shown in Table~\ref{table:compare_sota}. In the ResNet101 setting, the proposed methods outperform all other one-, two-, or multi-stage methods. In multi-stage methods, the proposed method surpasses other methods by a large margin. These experimental results clearly show the advantage of the proposed method.
\subsection{Learning curves of FPN and DSFPN}
To study the effectiveness of the proposed dually supervised (DS) training strategy in facilitating model learning, we leverage Mask RCNN with ResNet50 as detector and compute the AP$_{bbox}$ over training data and validation data (val2017) with interval $5,000$ iterations in $1\times$ training setting. Due to the large volume of training data, we compute the metric over randomly sampled $10k$ images from entire training images.
From Fig.~\ref{fig:learning_curve}, we note that the detector with  DSFPN converges faster than that with FPN in training data (red solid line vs blue solid line). This can be attributed to DS training manner. Specifically, DS introduces auxiliary prediction heads to assist the final prediction heads via directly supervising bottom-up subnet. Therefore, the detector with DSFPN can converge more efficiently. Moreover, the detector with DSFPN can converge to a higher AP{$_{bbox}$} point. This demonstrates that DS not only promotes detector training efficiently but also effectively.
\begin{figure}[t]
	\begin{center}
	\includegraphics[width=.951\linewidth,height=.6\linewidth]{fig/learning_curve.pdf}
	\end{center}
	\vspace{-5.5mm}
	\caption{Learning curves of detectors with FPN (blue) and DSFPN (red). The detector is Mask RCNN with ResNet50, trained in $1\times$ setting.}
	\label{fig:learning_curve}
\end{figure}
\section{Conclusion}
In this work, we propose a novel dually supervised training strategy, named DSFPN, to improve FPN~\cite{lin2017feature} by dealing with the gradient exploding or vanishing problem. DSFPN is generally effective for object detection and segmentation tasks, and it needs little extra computation during detection/segmentation inference.
In addition, a simple but effective \textit{cls}-\textit{reg} decoupled head is proposed for object detection in DSFPN to alleviate task inconsistency. To demonstrate the superiority, generalizability, and efficiency of the proposed DSFPN, we integrate DSFPN into Faster/Mask/Cascade/CascadeMask RCNN, and promising results are observed in the extensive validation experiments on the COCO dataset.
\label{conclusion}

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


CCIE is called the PHD in networking. The CCIE holders are certified in one or more aspects of networking services. The grades in this certification include routing and switching, network security, VOIP etc. The CCIE certification includes gruel some training session and a written examination. But the hardest part of the whole procedure is the CCIE Labs. It is an eight hour long onsite test that determines your network efficiency. Only by passing this test the CCIE certification can be achieved. This makes the CISCO certification exam the most widely accepted certification worldwide.
In order to make success in the CCIE labs you will have no shortcut methods. You will not be able to use any cheats or online help. The only help you will receive is the CISCO documentation CD which will have some of the network configurations of the site. Each exam will be different form one another as the test is conducted onsite. CISCO considers the CCIE certification as one of its best services to the world. The CCIE networking experts can easily handle most of the issues inside the home network. In addition to this they have easy access to the support teams within CISCO. This makes them highly reliable.
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Chat with Dean Gillian Lester while grabbing breakfast on your way to class or to study.
This roundtable will cover the difference between appellate and district clerkships and other topics.
Come learn more about the debate about NYC's specialized high schools and the removal of the SHSAT!
Join SKLS for a lunch panel with North Korean defectors discussing human rights issues related to North Korea.


text:
Q: To pass dynamic inputs for "within" parameter in the ezAnova Function in R I know the number of inputs & inputs for "within" parameter during runtime for the ezAnova function. I need to create the generic R function with the selected inputs.
library(ez)
Repeated_Measurement_Anova_function <- function(input, withinVal) {
  InputRA <- data.frame(input)
  wVal <- paste(unlist(withinVal), collapse = ',')
  result = ezANOVA(data = InputRA, dv = col1, wid = col2, within =.(wVal), between = col3)
  print(result)
}

it ends with the "wVal" is not a variable in the data frame provided.
I've tried wrapping the WVal with eval() but ends with same error.

redpajama_set_name: RedPajamaStackExchange


text:
Castles Technology Appoints Ben Love As CEO for North America Headquarters
By Atlanta Tech News January 5, 2023 January 5, 2023 LatestAtlanta, Atlanta Tech News, Castles, Castles Technology, Executives, Georgia, Global, International, Leadership, North America, Technology, Technology Headlines
Castles Technology, a leading global payment solutions provider, today announces that North American President and Chief Technology Officer Ben Love will become the Chief Executive Officer for North America Headquarters, effective January 1, 2023.
Current North American CEO Winston Fong will serve on the board of Castles North America as Vice Chairman and resume his Chief Strategy Officer position in the global Castles Technology organization,
Ben returned to Castles Technology in July 2022 and brings twenty-five years of international experience in key roles to deliver foundational industry change in payment acceptance, advancing the state of point of sale, and building and leading teams to success in fintech startups and large global organizations.
"After thorough planning and a successful transition process, I am pleased to have Ben Love assume leadership of this key region in our global organization. We are most grateful to Winston Fong for his entrepreneurship and dedication over the past seven years, which have laid a solid foundation for continued growth and success in North America. Ben's mission is to build upon this to serve the needs of our local and global clients and extend our world-class value to new market segments in the region," said Kevin Hsin, Chairman of Castles Technology Headquarters, Taiwan.
"I am honored and grateful to Kevin, Winston, and Castles Technology for the opportunity to lead this exceptional team of creative, dedicated, and talented professionals in North America," said Ben Love. "Winston is a remarkable leader who has successfully grown the company's presence in the USA, Canada, Mexico, and the Caribbean. My goal is to build on his achievements over the past seven years. Castles Technology will continue to bring our clients new, secure payment technologies and innovative and value-generating solutions as a trusted partner in our market."
Before rejoining Castles, Ben had been involved in both sides of the Bank of America and Visa partnership to build and deliver the Bank of America's new acquiring platform to market. Previously, he was co-founder of fintech start-up Kinetic Smart Solutions and has held Vice President roles across Product Management and technology at Clover, First Data (now FISERV), Vantiv (now FIS), and Ingenico.
Stephan Jan, Castles Technology International, 1 8322986612, [email protected]
Ben Love, Castles Technology International, 4044063281, [email protected]
Exploring Opportunities From Technology and Conventional Jobs
Nexus Circular Secures $150 Million to Accelerate Momentum of Advanced Recycling Solution for Plastics


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At 'Arizona Storytellers: Searching,' whether it's money, love, meaning or car keys – we're all searching for something.
Whether it's money, love, meaning or car keys – we're all searching for something.
"I feel like as a 20-something I'm in a state of searching for the next things in life, that search really changed when my mom died," said Becca Smouse, one of five people who will share their experience of "Searching" at the next Arizona Storytellers evening.
The event is Sept. 13 at Desert Botanical Garden.
Smouse, 23, of Tempe, lost her mother just before graduating from Arizona State University. When most young adults celebrate starting a new chapter and forging a career, Smouse began navigating her future without her mom.
"It's really personal, and (Storytellers) is just a really great platform to carry on my mom's legacy and tell her story," said Smouse, who works for Tempe High School District and is a former producer for azcentral.
Amy Carney, who will share what she searched for while being an adoptive mom.
Shawn Jordan, who had some difficulty getting mannequins for a wearable electronics class.
Anwar Newton, who shares a story about being carjacked on his way home from work.
In an effort to be more accessible to guests who are deaf or have hearing loss, the Arizona Storytellers Project is providing live, open captioning at all remaining shows in 2018.
There will be a section of reserved seats in front of the screen. Anyone who wants to use the service is welcome to sit in the reserved section, as is any of their guests.
More than 1 million Arizonans have some form of hearing loss – captioning ensures more people can enjoy our events.
The Storytellers Project helps people learn to tell powerful, moving stories.
To ask about supporting partnerships, contact michael.heller@gannett.com.


text:
"""
A Simple example for testing the SimpleServer Class. A simple connectionless
server. It is for studying purposes only.
"""

from simple.server import SimpleServer

__author__ = "Facundo Victor"
__license__ = "MIT"
__email__ = "facundovt@gmail.com"

def handle_message(sockets=None):
    """
    Handle a simple UDP client.
    """
    if sockets is not None:
        (readable, writable, errors) = sockets
        try:
            while True:
                (data, address) = readable.recvfrom(1024)
                print('Received data: %s from %s' % (data, address))
                if data:
                    print('Sending a custom ACK to the client %s'
                          % (address.__str__()))
                    writable.sendto(data, address)
                else:
                    print('Received empty data')
                    break
        finally:
            SS.close_connection()

SS = SimpleServer(connection_oriented=False)
SS.register_handler(handle_message)
SS.bind_and_listeen("localhost", 8888)

redpajama_set_name: RedPajamaGithub